Reporting of race and ethnicity in studies of artificial intelligence in pediatric urology

A secondary analysis of the AI-PEDURO online repository

Authors

  • Hyunwoong Harry Chae University of British Columbia
  • David-Dan Nguyen Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada; Schwartz Reisman Institute for Technology and Society, University of Toronto, Toronto, ON, Canada
  • Adree Khondker Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, The Hospital for Sick Children, Toronto, ON, Canada.
  • Mandy Rickard Division of Urology, The Hospital for Sick Children, Toronto, ON, Canada.
  • Lauren Erdman Temerty Centre for AI Research and Education in Medicine, University of Toronto, Toronto, ON, Canada; James M. Anderson Center for Health Systems Excellence, Cincinnati Children’s Hospital Medical Center and University of Cincinnati School of Medicine, Cincinnati, OH, USA
  • Andrew T. Gabrielson James Buchanan Brady Urological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Jin Kyu Kim Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, The Hospital for Sick Children, Toronto, ON, Canada.; Division of Urology, Riley Children’s Hospital, Indianapolis, IN, USA.
  • Jethro CC. Kwong Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada
  • Brian Chun Division of Urology, Children’s Hospital of Los Angeles, CA, USA.
  • Tariq Abbas Division of Urology, Sidra Medicine, Doha, Qatar
  • Nicolas Fernandez Division of Pediatric Urology, Seattle Children’s Hospital, Seattle, WA, USA.
  • Katherine Fischer Division of Urology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA.
  • Lisette A. 't Hoen Department of Urology, Sophia Children’s Hospital, Erasmus University Medical Center, Rotterdam, The Netherlands
  • Daniel T. Keefe Department of Urology, IWK Hospital, Halifax, NS, Canada.
  • Hsin-Hsiao (Scott) Wang Department of Urology, Boston Children’s Hospital, Boston, MA, USA
  • John Weaver Department of Urology, Cleveland Clinic Children’s Hospital, Cleveland, OH, USA
  • Armando J. Lorenzo Division of Urology, Department of Surgery, University of Toronto, Toronto, ON, Canada; Division of Urology, The Hospital for Sick Children, Toronto, ON, Canada.
  • Caleb P. Nelson Department of Urology, Boston Children’s Hospital, Boston, MA, USA

DOI:

https://doi.org/10.5489/cuaj.9541

Keywords:

Artificial intelligence, Machine learning, Pediatric Urology, Race, ethnicity

Abstract

Introduction: While an increasing number of artificial intelligence (AI) models are being developed in pediatric urology, the extent of race/ethnicity reporting among these studies is unclear. Our objective was to evaluate the inclusion and quality of race/ethnicity reporting in AI models in pediatric urology.

Methods: We conducted a secondary analysis of studies included in the AI in PEDiatric UROlogy (AI-PEDURO) collaborative living scoping review and repository. We examined racial/ethnic groups reported, their proportional representation, use of race/ethnicity as a predictor, conducting of stratified analyses by race/ethnicity, data collection methods, bias evaluation, and discussions of implications on equity.

Results: Of 81 studies in the AI-PEDURO repository, six (7.4%) reported race/ethnicity. Five studies included White and Black patients, representing 4824/7968 (60.5%) and 1377/7968 (17.3%) of the pooled cohort, respectively. Asian patients were included in three studies and represented 178/6861 (2.6%). Two studies reported Native Hawaiian or other Pacific Islander and Hispanic or Latino patients, representing 20/6704 (0.3%) and 1236/6704 (18.4%), respectively. One study included American Indian or Alaska Native patients, representing 69/6604 (1.0%). Mixed patients were included in three studies and represented 103/7711 (1.3%). Race/ethnicity was a predictor variable in 4/6 studies. None of these six studies conducted stratified analyses of model performance across race/ethnicity subgroups, reported race/ethnicity data collection methodologies, examined algorithmic biases, discussed implications on equity, or examined socioeconomic status or geographic residence.

Conclusions: Race/ethnicity reporting is poor in most AI studies in pediatric urology. Standardized reporting may help ensure fairness and generalizability of models across diverse pediatric urology populations.

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Published

2026-03-30

How to Cite

Chae, H. H., Nguyen, D.-D., Khondker, A., Rickard, M., Erdman, L., Gabrielson, A. T., … Nelson, C. P. (2026). Reporting of race and ethnicity in studies of artificial intelligence in pediatric urology: A secondary analysis of the AI-PEDURO online repository. Canadian Urological Association Journal, 20(8). https://doi.org/10.5489/cuaj.9541

Issue

Vol. 20 No. 8 (2026): CUAJ August

Section

Review

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